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Jacob Geleijns - One of the best experts on this subject based on the ideXlab platform.

Michael Davies - One of the best experts on this subject based on the ideXlab platform.

  • robust 3d Reconstruction of dynamic scenes from single photon lidar using beta divergences
    arXiv: Image and Video Processing, 2020
    Co-Authors: Quentin Legros, Julian Tachella, Rachael Tobin, Aongus Mccarthy, Sylvain Meignen, Gerald S Buller, Yoann Altmann, S Mclaughlin, Michael Davies
    Abstract:

    In this paper, we present a new algorithm for fast, online 3d Reconstruction of dynamic scenes using times of arrival of photons recorded by single-photon detector arrays. One of the main challenges in 3d imaging using single-photon lidar in practical applications is the presence of strong ambient illumination which corrupts the data and can jeopardize the detection of peaks/surface in the signals. This background noise not only complicates the observation model classically used for 3d Reconstruction but also the estimation procedure which requires iterative methods. In this work, we consider a new similarity measure for robust depth estimation, which allows us to use a simple observation model and a non-iterative estimation procedure while being robust to mis-specification of the background illumination model. This choice leads to a computationally attractive depth estimation procedure without significant degradation of the Reconstruction performance. This new depth estimation procedure is coupled with a spatio-temporal model to capture the natural correlation between neighboring pixels and successive frames for dynamic scene analysis. The resulting online inference process is scalable and well suited for parallel implementation. The benefits of the proposed method are demonstrated through a series of experiments conducted with simulated and real single-photon lidar videos, allowing the analysis of dynamic scenes at 325 m observed under extreme ambient illumination conditions.

  • fast online 3d Reconstruction of dynamic scenes from individual single photon detection events
    IEEE Transactions on Image Processing, 2020
    Co-Authors: Yoann Altmann, S Mclaughlin, Michael Davies
    Abstract:

    In this paper, we present an algorithm for online 3d Reconstruction of dynamic scenes using individual times of arrival (ToA) of photons recorded by single-photon detector arrays. One of the main challenges in 3d imaging using single-photon Lidar is the integration time required to build ToA histograms and reconstruct reliably 3d profiles in the presence of non-negligible ambient illumination. This long integration time also prevents the analysis of rapid dynamic scenes using existing techniques. We propose a new method which does not rely on the construction of ToA histograms but allows, for the first time, individual detection events to be processed online, in a parallel manner in different pixels, while accounting for the intrinsic spatiotemporal structure of dynamic scenes. Adopting a Bayesian approach, a Bayesian model is constructed to capture the dynamics of the 3d profile and an approximate inference scheme based on assumed density filtering is proposed, yielding a fast and robust Reconstruction algorithm able to process efficiently thousands to millions of frames, as usually recorded using single-photon detectors. The performance of the proposed method, able to process hundreds of frames per second, is assessed using a series of experiments conducted with static and dynamic 3d scenes and the results obtained pave the way to a new family of real-time 3d Reconstruction solutions.

  • fast online 3d Reconstruction of dynamic scenes from individual single photon detection events
    arXiv: Image and Video Processing, 2019
    Co-Authors: Yoann Altmann, S Mclaughlin, Michael Davies
    Abstract:

    In this paper, we present an algorithm for online 3d Reconstruction of dynamic scenes using individual times of arrival (ToA) of photons recorded by single-photon detector arrays. One of the main challenges in 3d imaging using single-photon Lidar is the integration time required to build ToA histograms and reconstruct reliable 3d profiles in the presence of non-negligible ambient illumination. This long integration time also prevents the analysis of rapid dynamic scenes using existing techniques. We propose a new method which does not rely on the construction of ToA histograms but allows, for the first time, individual detection events to be processed online, in a parallel manner in different pixels, while accounting for the intrinsic spatiotemporal structure of dynamic scenes. Adopting a Bayesian approach, a Bayesian model is constructed to capture the dynamics of the 3d profile and an approximate inference scheme based on assumed density filtering is proposed, yielding a fast and robust Reconstruction algorithm able to process efficiently thousands to millions of frames, as usually recorded using single-photon detectors. The performance of the proposed method, able to process hundreds of frames per second, is assessed using a series of experiments conducted with static and dynamic 3d scenes and the results obtained pave the way to a new family of real-time 3d Reconstruction solutions.

Pepijn Kamminga - One of the best experts on this subject based on the ideXlab platform.

Paul W. De Bruin - One of the best experts on this subject based on the ideXlab platform.

Martin Brazeau - One of the best experts on this subject based on the ideXlab platform.